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Abstract

A method is described for adaptively estimating the probability of occurrence of a symbol from a binary nonstationary source. The method comprises the steps of (1) assigning an initial value to said probability; and (2) updating said probability value at the occurrence of each symbol, the updating being dependent on a combination of the occurred symbol and a Monte Carlo function.

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English (United States)

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Adaptive Model for Nonstationary Sources

A method is described for adaptively estimating the probability of occurrence
of a symbol from a binary nonstationary source. The method comprises the steps
of (1) assigning an initial value to said probability; and (2) updating said
probability value at the occurrence of each symbol, the updating being
dependent on a combination of the occurred symbol and a Monte Carlo function.

Let qk(s) denote the probability of occurrence of the least probable symbol
(LPS) of a binary nonstationary source after a string s of symbols is observed.
With little loss in performance, the probability of occurrence of the LPS can be
restri to a finite set [Q1(S),Q2(S),...,QK(S)] where q1(s) = 1/2 and qi(s) >
q(i+1)(s). If the next symbol x observed after s is an LPS, then the assumed
value of qk(s) may need to be increased. However, the occurrence of an LPS in
x also depends on a Monte Carlo function. Therefore, q should be increased if
the next symbol x observed is an LPS and if qk(s) is equal to or less than a
random variable y having a uniform distribution over the interval [0,1].
Conversely, qk(s) should be decreased if the next symbol x observed is a most
probable symbol (MPS) and if qk(s) is also greater than a Monte Carlo generated
random variable y having a uniform distribution over the interval [0,1]. In general,
the probability of occurrence of the LPS is given by the following: qk(sx) = q(k-
1)(s) if x = LPS and y < qk(s) _ q(k+1)(s...